in-class static mapping

This week is semi-experimental

• Readings/demos: Lovelace, Ch 9 (https://geocompr.robinlovelace.net/adv-map.html#static-maps)
• Walks through:
  • Fills/shades
  • Borders
  • Multiple layers
  • Color settings
  • Layouts
  • Facets
  • Insets

Load relevant packages

library(sf)

## Warning: package 'sf' was built under R version 4.4.1

## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

library(terra)

## Warning: package 'terra' was built under R version 4.4.1

## terra 1.8.10

library(tmap)

## Warning: package 'tmap' was built under R version 4.4.1

library(spData) # new package - you'll likely need to install

## Warning: package 'spData' was built under R version 4.4.1

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks terra::extract()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Let’s talk about non-CRAN packages

# next line is commented out becasue you only need to install once
#install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", type = "source")

library(spDataLarge)

We’ll use this later

nz_elev = rast(system.file("raster/nz_elev.tif", package = "spDataLarge"))

tmap uses a similar “grammar of graphics” as ggplot2

simple fill

# nz data comes from spData package

tm_shape(nz) +
  tm_fill() 

or…

tm_shape(nz) +
  tm_borders()

Or layer two different symbologies

tm_shape(nz) +
  tm_fill() +
  tm_borders()

But keep in mind - ORDER MATTERS!!!

# flip the order to illustrate order matters
tm_shape(nz) +
  tm_borders() +
    tm_fill()

Add layers and assign your map to a variable

map_nz_base = tm_shape(nz) + tm_polygons()

map_nz_blue_dots = map_nz_base +
  tm_shape(nz_height) + tm_dots(size = 0.25, fill = "blue")

What happened? Nothing

You can simply call the variable to plot it

map_nz_blue_dots

It can be useful to “reuse” the base of a map and add different layers.

WAIT - Let’s first break down this code

# reuse map_nz_base from above

map_nz_red_bubbles = map_nz_base +
  tm_shape(nz_height) + tm_bubbles(size = "elevation", 
                                   fill = "darkred", 
                                   lwd = 0, 
                                   fill_alpha = .4)


map_nz_red_bubbles

Things to note: 1. dots vs. bubbles 2. fill vs. col (read the documentation) 3. variables used to change symbology are put inside ““: size = "elevation"

The utility of this sort of grammar

map <- base + layer_1 + layer_2 + aesthetic + function + layer_3

We’re going to leverage this flexibility to break down a mapping task into constituent parts

More examples

tm_shape(nz) + tm_polygons(fill = "Median_income")

Wait… how did it determine the breaks in the data to create the choropleth?

DON’T MAKE ASSUMPTIONS FOR THE DEFAULT BEHAIVOR!!!

Let’s create a new object that represents NZ’s territorial waters. Who want to help break down the code?

nz_water = st_union(nz) %>%
     st_buffer(22200) %>%
     st_cast(to = "LINESTRING")

How would we know what units the 22200 is in for the buffer function?

Plot it to show our work

tm_shape(nz_water) + tm_lines()

Let’s build a map in pieces

First, the base map and elevation

map1 <- tm_shape(nz) + tm_polygons() +
  tm_shape(nz_elev) + tm_raster(col_alpha = 0.7)

map1

Next, let’s setup the water boundaries

map2 <- tm_shape(nz_water) + tm_lines()

map2

Finally, we can symbolize the heighest points

map3 <- tm_shape(nz_height) + tm_symbols()

map3

And then put it all together…

map.all <- map1 + map2 + map3

map.all

From your readings: How to arrange multiple maps into a single plot

map.arr <- tmap_arrange(map1, map2, map3)

map.arr

Any issues here?

TONS more options

Let’s go through one-by-one. What are your expectations fior each???

ma1 = tm_shape(nz) + tm_polygons(fill = "red")
ma2 = tm_shape(nz) + tm_polygons(fill = "red", fill_alpha = 0.3)
ma3 = tm_shape(nz) + tm_polygons(col = "blue")
ma4 = tm_shape(nz) + tm_polygons(lwd = 3)
ma5 = tm_shape(nz) + tm_polygons(lty = 2)
ma6 = tm_shape(nz) + tm_polygons(fill = "red", fill_alpha = 0.3,
                                 col = "blue", lwd = 3, lty = 2)
tmap_arrange(ma1, ma2, ma3, ma4, ma5, ma6)

Choropleth mapping options

Setting your own breaks

tm_shape(nz) + tm_polygons(fill = "Median_income")

tm_shape(nz) + tm_polygons(fill = "Median_income",
                        fill.scale = tm_scale(breaks = c(0, 30000, 40000, 50000)))

A different way: 10 breaks

tm_shape(nz) + tm_polygons(fill = "Median_income",
                           fill.scale = tm_scale(n = 10))

Tmap default with shading

tm_shape(nz) + tm_polygons(fill = "Median_income",
                           fill.scale = tm_scale(values = "BuGn"))

## [cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "BuGn" is named
## "brewer.bu_gn"
## Multiple palettes called "bu_gn" found: "brewer.bu_gn", "matplotlib.bu_gn". The first one, "brewer.bu_gn", is returned.

Standard methods

Let’s go one by one

Equal interval

tm_shape(nz) + tm_polygons(fill = "Median_income",
                           fill.scale = tm_scale_intervals(style = "equal", values = "BuGn"))

## [cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "BuGn" is named
## "brewer.bu_gn"
## Multiple palettes called "bu_gn" found: "brewer.bu_gn", "matplotlib.bu_gn". The first one, "brewer.bu_gn", is returned.

Jenks

tm_shape(nz) + tm_polygons(fill = "Median_income",
                           fill.scale = tm_scale_intervals(style = "jenks", values = "viridis"))

Quantiles

tm_shape(nz) + tm_polygons(fill = "Median_income",
                           fill.scale = tm_scale_intervals(style = "quantile", values = "brewer.purples", n = 5))

Legends

Let’s start simple: changing the title

legend_title = expression("Area (km"^2*")")
tm_shape(nz) +
  tm_polygons(fill = "Land_area", fill.legend = tm_legend(title = legend_title))

A bit more complicated: title, orientation, and position

tm_shape(nz) +
  tm_polygons(fill = "Land_area",
              fill.legend = tm_legend(title = legend_title,
                                      orientation = "landscape",
                                      position = tm_pos_out("center", "bottom")))

## [plot mode] fit legend/component: Some legend items or map compoments do not
## fit well, and are therefore rescaled.
## ℹ Set the tmap option `component.autoscale = FALSE` to disable rescaling.

We can of course pick and choose: let’s keep the legend in “portrait mode”

tm_shape(nz) +
  tm_polygons(fill = "Land_area",
              fill.legend = tm_legend(title = legend_title,
                                      position = tm_pos_out("center", "bottom")))

Lastly, we can use tmap modes to be more interactive if we want

tmap_mode(mode = "view")

## ℹ tmap mode set to "view".

tm_shape(nz) +
  tm_polygons(fill = "Land_area",
              fill.legend = tm_legend(title = legend_title,
                                      position = tm_pos_out("center", "bottom")))

ttm()

## ℹ tmap mode set to "plot".

Try clicking on a feature - what happens?

There is a LOT more detail in your readings for today, which we’re going to leverage in your next activity:

Activity: Building a frankenmap

width:750px

1. I will break the class into three groups
2. As separate groups, you’ll accomplish series of cartographic tasks
3. Then we’ll bring them together at the end and code up a single visualization

Data:

• in our GitHub repo
• ./data/static_mapping/
• you WILL need to use these data to derive some other products (e.g., smaller rasters, joined census data)

GROUP 1 - Ohio scale

1. Ohio counties, symbolized (filled) by some variable of interest. You will need to use a tabular join (left_join(), most likely) between spatial and tabular data
2. Borders symbolized using NON-DEFAULT symbols
3. A scale bar

GROUP 2 - Local scale

1. Municipal boundaries within Portage AND Summit counties, with labels for names (see oh_places.gpkg)
2. Parks within Portage AND Summit counties, symbolized using different shades of green according to the park TYPE
3. Linear water features (streams, rivers) in Portage AND Summit counties. Symbols should indicate which linear features intersect a park

GROUP 3 - Putting it together

1. Use the provided DEM to plot elevation behind a semi-transparent Portage and Summit counties
2. A north arrow
3. Code to make Group 1’s code (all of Ohio) an inset into Group 2’s Portage + Summit counties study area
4. A title

One NOTE:

EVERYTHING NEEDS DONE PROGRAMMATICALLY REFERENCING DATA LOCATIONS IN THE REPO (YOU’LL SEE WHY LATER)

BEFORE YOU START. WHAT ARE YOUR GENERAL STEPS???

You have until ______________________:

Now that we have the components, what’s next?

That’s right, let’s put it together.

1. Share the code
2. Someone needs to piece it all together
3. We’ll share to the class when we’re done